Machine learning driven prediction of mechanical properties of rolled aluminum and development of an in-situ quality control method based on electrical resistivity measurement

Karin Hartl; Marcel Sorger; Helmut Weiß; Martin Stockinger

doi:10.1016/j.jmapro.2023.09.058

Machine learning driven prediction of mechanical properties of rolled aluminum and development of an in-situ quality control method based on electrical resistivity measurement

Karin Hartl, Marcel Sorger, Helmut Weiß, Martin Stockinger

Research output: Contribution to journal › Article › Research › peer-review

Original language	English
Pages (from-to)	158-177
Number of pages	20
Journal	Journal of manufacturing processes
Volume	106.2023
Issue number	24 November
DOIs	https://doi.org/10.1016/j.jmapro.2023.09.058
Publication status	E-pub ahead of print - 5 Oct 2023

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10.1016/j.jmapro.2023.09.058

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Hartl, K., Sorger, M., Weiß, H., & Stockinger, M. (2023). Machine learning driven prediction of mechanical properties of rolled aluminum and development of an in-situ quality control method based on electrical resistivity measurement. Journal of manufacturing processes, 106.2023(24 November), 158-177. Advance online publication. https://doi.org/10.1016/j.jmapro.2023.09.058

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Machine learning driven prediction of mechanical properties of rolled aluminum and development of an in-situ quality control method based on electrical resistivity measurement. / Hartl, Karin; Sorger, Marcel; Weiß, Helmut et al.
In: Journal of manufacturing processes, Vol. 106.2023, No. 24 November, 05.10.2023, p. 158-177.

Research output: Contribution to journal › Article › Research › peer-review

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T1 - Machine learning driven prediction of mechanical properties of rolled aluminum and development of an in-situ quality control method based on electrical resistivity measurement

AU - Hartl, Karin

AU - Sorger, Marcel

AU - Weiß, Helmut

AU - Stockinger, Martin

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EP - 177

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JF - Journal of manufacturing processes

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